论文标题
裂开：从激光雷达和视觉检测3D对象检测
RoIFusion: 3D Object Detection from LiDAR and Vision
论文作者
论文摘要
当定位和检测3D对象以用于自动驾驶场景时，从多个传感器（例如相机，LIDAR）获取信息通常会增加3D检测器的鲁棒性。但是，从激光雷达和相机捕获的不同特征的有效融合仍然具有挑战性，尤其是由于点云分布的稀疏性和不规则性。尽管如此，点云提供了有用的互补信息。在本文中，我们想通过提出深层神经网络体系结构来利用LiDAR和相机传感器的优势，以通过识别其方向的相应的3D边界框来融合和有效检测3D对象。为了实现这一任务，我们通过将一组从点云从点云投射到相应图像的2D ROI的3D区域（ROI）来提出一种新颖的融合算法，而不是密集地结合点云的点特征和相关的像素功能。最后，我们证明了我们的深层融合方法在KITTI 3D对象检测上实现了最先进的性能，具有挑战性的基准。
When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensor (e.g. camera, LIDAR) typically increases the robustness of 3D detectors. However, the efficient and effective fusion of different features captured from LIDAR and camera is still challenging, especially due to the sparsity and irregularity of point cloud distributions. This notwithstanding, point clouds offer useful complementary information. In this paper, we would like to leverage the advantages of LIDAR and camera sensors by proposing a deep neural network architecture for the fusion and the efficient detection of 3D objects by identifying their corresponding 3D bounding boxes with orientation. In order to achieve this task, instead of densely combining the point-wise feature of the point cloud and the related pixel features, we propose a novel fusion algorithm by projecting a set of 3D Region of Interests (RoIs) from the point clouds to the 2D RoIs of the corresponding the images. Finally, we demonstrate that our deep fusion approach achieves state-of-the-art performance on the KITTI 3D object detection challenging benchmark.
